Abstract

In this paper, we propose to use support vector machines for classification of bacterial growth and non growth database and modeling the probability= of growth. Unlike artificial neural networks paradigms, support vector machines use the kernel functions and support vectors with maximum margin, which allows a better performance. As a practical application of the new approach, support vector machines were investigated for their quality and accuracy in classifi-cation of growth/no-growth state of a pathogenic Escherichia coli R31 in response to temperature and water activity. A comparison with the most common used statistics, machine learning, and data mining schemes was carried out. The results shows that support vector machines classifier based on the Gaussian RBF Kernel was found to do better than most of logistic regression, K-nearest neighbor, probabilistic networks, and multilayer perceptron classifiers.